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Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

computer Modeling in Engineering & sciences 2025年第3期142卷 2487-2511页

作者： Asma Aldrees Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Mohd Anjum Department of Informatics and Computer Systems College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India

Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic *** disorders are a major global health concern,affecting millions of people worldwide due to their widespread *** photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic *** a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient *** address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic *** selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus *** filtering relies on the minimum derivative,determined by extracting both similar and varying *** this research,the DNN model was integrated with the derivative *** images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions *** goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous *** hidden layer of the neural network operates on the most significant derivative,which may reduce precision across *** derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding *** proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and ***,the propo

关键词： Deep neural network feature extraction fundus detection medical image processing

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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

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computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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computer Modeling in Engineering & sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Comparison of Machine Learning Algorithms for Predicting Thyroid Disorders in Diabetic Patients

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第12期49卷 105-114页

作者： Sayyid, Hiba O. Mahmood, Salma A. Hamadi, Saad S. Department of Computer Science University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Intelligent Medical Systems University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Internal Medicine University of Basrah College of Medicine Basrah Iraq

Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been used successfully in the healthcare domain for disease diagnosis. Thyroid disorders and diabetes are two of the most prevalent and interconnected chronic diseases, as both play critical roles in regulating various physiological processes in the body. This study aims to predict thyroid disorders in diabetes patients using six machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). A locally sourced dataset comprising 44,539 instances of diabetic patients was utilized, undergoing preprocessing steps including data cleaning, encoding, and balancing. Two balancing techniques were employed: manual balancing and RandomUnderSampler. The dataset was partitioned into training and testing sets using a Stratified K-Fold cross-validation approach with 10 folds to ensure robust evaluation. Each algorithm’s performance was assessed using metrics such as accuracy and F1-score. Among the models, the RF algorithm outperformed the others, achieving the highest accuracy of 95% on the manually balanced dataset and 84% when the RandomUnderSampler technique was employed. Additionally, the F1-scores for RF were 95% and 82%, respectively, indicating its robustness in handling imbalanced datasets. This study highlights the importance of selecting appropriate preprocessing techniques and machine learning methods for healthcare datasets. The findings can assist healthcare providers in making early diagnoses and interventions for thyroid disorders in diabetic patients, potentially improving their quality of life and overall healthcare outcomes. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Logistic regression

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Prediction Model for Coronavirus Pandemic Using Deep Learning

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computer systems science & Engineering 2022年第3期40卷 947-961页

作者： Mamoona Humayun Ahmed Alsayat Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science College of Computer and Information SciencesJouf UniversityKSA

The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life *** of the countries was ready to face this emerging health *** professionals were not able to predict its rise and next move,as well as the future curve and impact on lives in case of a similar pandemic situation *** created huge chaos globally,for longer and the world is still struggling to come up with any suitable *** the better use of advanced technologies,such as artificial intelligence and deep learning,may aid healthcare practitioners in making reliable COVID-19 *** proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia,and also empowering health professionals to distinguish chest CT scans of healthy *** efforts done by the Saudi government for the management and control of COVID-19 are remarkable,however;there is a need to improve the diagnostics process for better *** used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT *** proposed methodology was compared to current models and found to be more accurate(93 percent)than the existing methods.

关键词： Artificial Intelligence(AI) deep learning(DL) COVID-19 pandemic

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

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computers, Materials & Continua 2023年第1期74卷 399-414页

作者： Nora Abdullah Alkhaldi Hanan T.Halawani Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityP.O.Box 400AlAhsa31982Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Automated segmentation of blood vessels in retinal fundus images is essential for medical image *** segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal *** article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)*** proposed GOFED-RBVSC model initially employs contrast enhancement ***,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership *** ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature ***,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the *** performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

关键词： Edge detection blood vessel segmentation retinal fundus images image classification deep learning

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Context-aware automated quality assessment of textual data

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International Journal of Business Intelligence and Data Mining 2023年第4期22卷 451-469页

作者： Mylavarapu, Goutam Viswanathan, Kannan Ashwin Thomas, Johnson Department of Computer Science and Information Systems Murray State University Murray United States Department of Computer Science Oklahoma State University StillwaterOK United States

Data analysis is a crucial process in the field of data science that extracts useful information from any form of data. With the rapid growth of technology, more and more unstructured data, such as text and images, are being produced in large amounts. Apart from the analytical techniques used, the quality of the data plays a prominent role in the accurate analysis. Data quality becomes inferior to poor maintenance and mediocre data generation strategies employed by amateur users. This problem escalates with the advent of big data. In this paper, we propose a quality assessment model for the textual form of unstructured data (TDQA). The context of data plays an important role in determining the quality of the data. Therefore, we automate the process of context extraction in textual data using natural language processing to identify data errors and assess quality. Copyright 2023 Inderscience Enterprises Ltd.

关键词： Sentiment analysis

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